Overview

Dataset statistics

Number of variables21
Number of observations8631418
Missing cells20084
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 GiB
Average record size in memory168.0 B

Variable types

Numeric14
Categorical7

Alerts

municipio has a high cardinality: 2327 distinct values High cardinality
causa_nombre has a high cardinality: 308 distinct values High cardinality
df_index is highly correlated with regionHigh correlation
region is highly correlated with df_indexHigh correlation
factor is highly correlated with pop and 2 other fieldsHigh correlation
pop is highly correlated with factor and 4 other fieldsHigh correlation
pop_male is highly correlated with factor and 4 other fieldsHigh correlation
pop_female is highly correlated with factor and 4 other fieldsHigh correlation
afromexican is highly correlated with pop and 3 other fieldsHigh correlation
metro_area is highly correlated with pop and 3 other fieldsHigh correlation
long is highly correlated with latHigh correlation
lat is highly correlated with longHigh correlation
df_index is highly correlated with regionHigh correlation
region is highly correlated with df_indexHigh correlation
pop is highly correlated with pop_male and 3 other fieldsHigh correlation
pop_male is highly correlated with pop and 3 other fieldsHigh correlation
pop_female is highly correlated with pop and 3 other fieldsHigh correlation
afromexican is highly correlated with pop and 2 other fieldsHigh correlation
metro_area is highly correlated with pop and 2 other fieldsHigh correlation
long is highly correlated with latHigh correlation
lat is highly correlated with longHigh correlation
df_index is highly correlated with regionHigh correlation
region is highly correlated with df_indexHigh correlation
factor is highly correlated with pop and 2 other fieldsHigh correlation
pop is highly correlated with factor and 3 other fieldsHigh correlation
pop_male is highly correlated with factor and 3 other fieldsHigh correlation
pop_female is highly correlated with factor and 3 other fieldsHigh correlation
afromexican is highly correlated with pop and 2 other fieldsHigh correlation
metro_area is highly correlated with state_nameHigh correlation
state_name is highly correlated with metro_areaHigh correlation
df_index is highly correlated with region and 4 other fieldsHigh correlation
region is highly correlated with df_index and 3 other fieldsHigh correlation
edad is highly correlated with escolaridad and 2 other fieldsHigh correlation
escolaridad is highly correlated with edad and 1 other fieldsHigh correlation
edo_civil is highly correlated with edad and 1 other fieldsHigh correlation
state_name is highly correlated with df_index and 8 other fieldsHigh correlation
pop is highly correlated with state_name and 6 other fieldsHigh correlation
pop_male is highly correlated with state_name and 6 other fieldsHigh correlation
pop_female is highly correlated with state_name and 5 other fieldsHigh correlation
afromexican is highly correlated with state_name and 5 other fieldsHigh correlation
indigenous_language is highly correlated with afromexicanHigh correlation
metro_area is highly correlated with state_name and 4 other fieldsHigh correlation
long is highly correlated with df_index and 6 other fieldsHigh correlation
lat is highly correlated with df_index and 5 other fieldsHigh correlation
esco_avail is highly correlated with escolaridadHigh correlation
death is highly correlated with df_index and 2 other fieldsHigh correlation
factor is highly skewed (γ1 = 59.11565334) Skewed
df_index has unique values Unique
escolaridad has 98201 (1.1%) zeros Zeros
edo_civil has 140746 (1.6%) zeros Zeros

Reproduction

Analysis started2021-12-23 20:39:13.848948
Analysis finished2021-12-23 20:59:24.195636
Duration20 minutes and 10.35 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8631418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4316465.193
Minimum0
Maximum8643310
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size65.9 MiB
2021-12-23T17:59:24.284548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile431585.85
Q12157953.25
median4315940.5
Q36473880.75
95-th percentile8206121.15
Maximum8643310
Range8643310
Interquartile range (IQR)4315927.5

Descriptive statistics

Standard deviation2492683.207
Coefficient of variation (CV)0.5774825224
Kurtosis-1.19875468
Mean4316465.193
Median Absolute Deviation (MAD)2157964
Skewness0.00108087599
Sum3.725721536 × 1013
Variance6.213469572 × 1012
MonotonicityStrictly increasing
2021-12-23T17:59:24.421183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83927021
 
< 0.1%
59315151
 
< 0.1%
59397111
 
< 0.1%
3466261
 
< 0.1%
45388831
 
< 0.1%
3548221
 
< 0.1%
45470791
 
< 0.1%
3302501
 
< 0.1%
45225071
 
< 0.1%
3384461
 
< 0.1%
Other values (8631408)8631408
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
86433101
< 0.1%
86433091
< 0.1%
86433081
< 0.1%
86433071
< 0.1%
86433061
< 0.1%
86433051
< 0.1%
86433041
< 0.1%
86433031
< 0.1%
86433021
< 0.1%
86433011
< 0.1%

region
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2503
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18414.77584
Minimum1001
Maximum35999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.9 MiB
2021-12-23T17:59:24.551816image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile7012
Q113038
median19021
Q324025
95-th percentile31015
Maximum35999
Range34998
Interquartile range (IQR)10987

Descriptive statistics

Standard deviation7784.733044
Coefficient of variation (CV)0.4227438397
Kurtosis-0.843234033
Mean18414.77584
Median Absolute Deviation (MAD)5951
Skewness0.04358069972
Sum1.589456276 × 1011
Variance60602068.56
MonotonicityNot monotonic
2021-12-23T17:59:24.667121image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
900723450
 
0.3%
1403922261
 
0.3%
200421991
 
0.3%
1102021281
 
0.2%
1503321250
 
0.2%
900520952
 
0.2%
2111420051
 
0.2%
1412020039
 
0.2%
2033419495
 
0.2%
1903918645
 
0.2%
Other values (2493)8422003
97.6%
ValueCountFrequency (%)
100113835
0.2%
10024179
 
< 0.1%
10034792
 
0.1%
10042992
 
< 0.1%
10056005
0.1%
10065219
 
0.1%
10075881
0.1%
10082436
 
< 0.1%
10093724
 
< 0.1%
10103269
 
< 0.1%
ValueCountFrequency (%)
35999149
 
< 0.1%
34999216
 
< 0.1%
33999890
 
< 0.1%
329997
 
< 0.1%
320581680
 
< 0.1%
320572967
< 0.1%
320565359
0.1%
320553599
< 0.1%
320542915
< 0.1%
320532470
< 0.1%

sexo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.9 MiB
1
4555532 
2
4075886 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8631418
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
14555532
52.8%
24075886
47.2%

Length

2021-12-23T17:59:24.788369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-23T17:59:24.850203image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
14555532
52.8%
24075886
47.2%

Most occurring characters

ValueCountFrequency (%)
14555532
52.8%
24075886
47.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8631418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
14555532
52.8%
24075886
47.2%

Most occurring scripts

ValueCountFrequency (%)
Common8631418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
14555532
52.8%
24075886
47.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII8631418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14555532
52.8%
24075886
47.2%

edad
Real number (ℝ≥0)

HIGH CORRELATION

Distinct129
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.7295434
Minimum0
Maximum130
Zeros26183
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size65.9 MiB
2021-12-23T17:59:24.925501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q128
median41
Q357
95-th percentile80
Maximum130
Range130
Interquartile range (IQR)29

Descriptive statistics

Standard deviation19.71504586
Coefficient of variation (CV)0.4508404233
Kurtosis-0.4362560355
Mean43.7295434
Median Absolute Deviation (MAD)14
Skewness0.4265370832
Sum377447968
Variance388.6830331
MonotonicityNot monotonic
2021-12-23T17:59:25.053194image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30197752
 
2.3%
40190294
 
2.2%
25177716
 
2.1%
32173480
 
2.0%
28173254
 
2.0%
35171750
 
2.0%
42170766
 
2.0%
38170535
 
2.0%
26168929
 
2.0%
24168820
 
2.0%
Other values (119)6868122
79.6%
ValueCountFrequency (%)
026183
0.3%
19846
 
0.1%
26550
 
0.1%
37028
 
0.1%
47673
 
0.1%
56149
 
0.1%
610515
0.1%
715467
0.2%
819117
0.2%
920734
0.2%
ValueCountFrequency (%)
1302
 
< 0.1%
1283
 
< 0.1%
1271
 
< 0.1%
1261
 
< 0.1%
1253
 
< 0.1%
1231
 
< 0.1%
1223
 
< 0.1%
1212
 
< 0.1%
12024
< 0.1%
1197
 
< 0.1%

escolaridad
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.324594985
Minimum0
Maximum10
Zeros98201
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size65.9 MiB
2021-12-23T17:59:25.160950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q38
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.573371767
Coefficient of variation (CV)0.483299063
Kurtosis-1.023095148
Mean5.324594985
Median Absolute Deviation (MAD)2
Skewness-0.06826829763
Sum45958805
Variance6.622242253
MonotonicityNot monotonic
2021-12-23T17:59:25.253704image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
61774594
20.6%
31498299
17.4%
41413178
16.4%
81316079
15.2%
91087565
12.6%
1828987
9.6%
5335834
 
3.9%
7143180
 
1.7%
10133187
 
1.5%
098201
 
1.1%
ValueCountFrequency (%)
098201
 
1.1%
1828987
9.6%
22314
 
< 0.1%
31498299
17.4%
41413178
16.4%
5335834
 
3.9%
61774594
20.6%
7143180
 
1.7%
81316079
15.2%
91087565
12.6%
ValueCountFrequency (%)
10133187
 
1.5%
91087565
12.6%
81316079
15.2%
7143180
 
1.7%
61774594
20.6%
5335834
 
3.9%
41413178
16.4%
31498299
17.4%
22314
 
< 0.1%
1828987
9.6%

ocupacion
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.24943781
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.9 MiB
2021-12-23T17:59:25.347094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median9
Q399
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)93

Descriptive statistics

Standard deviation45.74028489
Coefficient of variation (CV)1.057592589
Kurtosis-1.837114704
Mean43.24943781
Median Absolute Deviation (MAD)6
Skewness0.3950931923
Sum373303976
Variance2092.173662
MonotonicityNot monotonic
2021-12-23T17:59:25.431902image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
993469221
40.2%
61028036
 
11.9%
9949565
 
11.0%
7764290
 
8.9%
2703328
 
8.1%
4610738
 
7.1%
8408702
 
4.7%
5385189
 
4.5%
3214624
 
2.5%
197725
 
1.1%
ValueCountFrequency (%)
197725
 
1.1%
2703328
 
8.1%
3214624
 
2.5%
4610738
 
7.1%
5385189
 
4.5%
61028036
 
11.9%
7764290
 
8.9%
8408702
 
4.7%
9949565
 
11.0%
993469221
40.2%
ValueCountFrequency (%)
993469221
40.2%
9949565
 
11.0%
8408702
 
4.7%
7764290
 
8.9%
61028036
 
11.9%
5385189
 
4.5%
4610738
 
7.1%
3214624
 
2.5%
2703328
 
8.1%
197725
 
1.1%

edo_civil
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.889877538
Minimum0
Maximum9
Zeros140746
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size65.9 MiB
2021-12-23T17:59:25.523604image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.69430449
Coefficient of variation (CV)0.550996312
Kurtosis-1.370688861
Mean4.889877538
Median Absolute Deviation (MAD)2
Skewness-0.3611461379
Sum42206577
Variance7.259276683
MonotonicityNot monotonic
2021-12-23T17:59:25.609802image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
81833363
21.2%
71804277
20.9%
11758342
20.4%
51370663
15.9%
2546851
 
6.3%
4536939
 
6.2%
6322038
 
3.7%
3289900
 
3.4%
0140746
 
1.6%
928299
 
0.3%
ValueCountFrequency (%)
0140746
 
1.6%
11758342
20.4%
2546851
 
6.3%
3289900
 
3.4%
4536939
 
6.2%
51370663
15.9%
6322038
 
3.7%
71804277
20.9%
81833363
21.2%
928299
 
0.3%
ValueCountFrequency (%)
928299
 
0.3%
81833363
21.2%
71804277
20.9%
6322038
 
3.7%
51370663
15.9%
4536939
 
6.2%
3289900
 
3.4%
2546851
 
6.3%
11758342
20.4%
0140746
 
1.6%

factor
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct4072
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.62117963
Minimum1
Maximum18236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.9 MiB
2021-12-23T17:59:25.721506image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q310
95-th percentile52
Maximum18236
Range18235
Interquartile range (IQR)8

Descriptive statistics

Standard deviation97.87782351
Coefficient of variation (CV)6.69424944
Kurtosis5572.111568
Mean14.62117963
Median Absolute Deviation (MAD)3
Skewness59.11565334
Sum126201513
Variance9580.068336
MonotonicityNot monotonic
2021-12-23T17:59:25.842182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12098982
24.3%
3944466
10.9%
2912449
10.6%
4821443
 
9.5%
5535009
 
6.2%
6400399
 
4.6%
7297179
 
3.4%
8256116
 
3.0%
9178505
 
2.1%
10154327
 
1.8%
Other values (4062)2032543
23.5%
ValueCountFrequency (%)
12098982
24.3%
2912449
10.6%
3944466
10.9%
4821443
 
9.5%
5535009
 
6.2%
6400399
 
4.6%
7297179
 
3.4%
8256116
 
3.0%
9178505
 
2.1%
10154327
 
1.8%
ValueCountFrequency (%)
182361
< 0.1%
173501
< 0.1%
167161
< 0.1%
165651
< 0.1%
162361
< 0.1%
161441
< 0.1%
159441
< 0.1%
158351
< 0.1%
158241
< 0.1%
156141
< 0.1%

state_name
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)< 0.1%
Missing2095
Missing (%)< 0.1%
Memory size65.9 MiB
Oaxaca
1086671 
Veracruz
812432 
México
735460 
Puebla
708819 
Jalisco
488502 
Other values (27)
4797439 

Length

Max length19
Median length7
Mean length8.15373454
Min length6

Characters and Unicode

Total characters70361209
Distinct characters48
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAguascalientes
2nd rowAguascalientes
3rd rowAguascalientes
4th rowAguascalientes
5th rowAguascalientes

Common Values

ValueCountFrequency (%)
Oaxaca1086671
 
12.6%
Veracruz812432
 
9.4%
México735460
 
8.5%
Puebla708819
 
8.2%
Jalisco488502
 
5.7%
Chiapas478354
 
5.5%
Michoacán424181
 
4.9%
Guerrero349265
 
4.0%
Hidalgo302968
 
3.5%
Yucatán296599
 
3.4%
Other values (22)2946072
34.1%

Length

2021-12-23T17:59:25.966849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oaxaca1086671
 
11.0%
mã©xico962508
 
9.7%
veracruz812432
 
8.2%
puebla708819
 
7.2%
jalisco488502
 
4.9%
chiapas478354
 
4.8%
michoacã¡n424181
 
4.3%
guerrero349265
 
3.5%
hidalgo302968
 
3.1%
yucatã¡n296599
 
3.0%
Other values (28)3988773
40.3%

Most occurring characters

ValueCountFrequency (%)
a12449491
17.7%
c5243917
 
7.5%
o4904733
 
7.0%
u4396478
 
6.2%
i4148013
 
5.9%
r3541987
 
5.0%
e3512576
 
5.0%
l2677080
 
3.8%
x2251700
 
3.2%
Ã2198873
 
3.1%
Other values (38)25036361
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter55021690
78.2%
Uppercase Letter11870897
 
16.9%
Space Separator1269749
 
1.8%
Other Symbol1051939
 
1.5%
Other Punctuation720780
 
1.0%
Other Number216163
 
0.3%
Format209991
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a12449491
22.6%
c5243917
9.5%
o4904733
 
8.9%
u4396478
 
8.0%
i4148013
 
7.5%
r3541987
 
6.4%
e3512576
 
6.4%
l2677080
 
4.9%
x2251700
 
4.1%
s2092429
 
3.8%
Other values (13)9803286
17.8%
Uppercase Letter
ValueCountFrequency (%)
Ã2198873
18.5%
M1544606
13.0%
C1235893
10.4%
O1086671
9.2%
P918810
7.7%
V812432
 
6.8%
G604859
 
5.1%
S528461
 
4.5%
J488502
 
4.1%
T466290
 
3.9%
Other values (10)1985500
16.7%
Space Separator
ValueCountFrequency (%)
1269749
100.0%
Other Symbol
ValueCountFrequency (%)
©1051939
100.0%
Other Punctuation
ValueCountFrequency (%)
¡720780
100.0%
Other Number
ValueCountFrequency (%)
³216163
100.0%
Format
ValueCountFrequency (%)
­209991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin66892587
95.1%
Common3468622
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a12449491
18.6%
c5243917
 
7.8%
o4904733
 
7.3%
u4396478
 
6.6%
i4148013
 
6.2%
r3541987
 
5.3%
e3512576
 
5.3%
l2677080
 
4.0%
x2251700
 
3.4%
Ã2198873
 
3.3%
Other values (33)21567739
32.2%
Common
ValueCountFrequency (%)
1269749
36.6%
©1051939
30.3%
¡720780
20.8%
³216163
 
6.2%
­209991
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII65963463
93.7%
None4397746
 
6.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a12449491
18.9%
c5243917
 
7.9%
o4904733
 
7.4%
u4396478
 
6.7%
i4148013
 
6.3%
r3541987
 
5.4%
e3512576
 
5.3%
l2677080
 
4.1%
x2251700
 
3.4%
s2092429
 
3.2%
Other values (33)20745059
31.4%
None
ValueCountFrequency (%)
Ã2198873
50.0%
©1051939
23.9%
¡720780
 
16.4%
³216163
 
4.9%
­209991
 
4.8%

municipio
Categorical

HIGH CARDINALITY

Distinct2327
Distinct (%)< 0.1%
Missing2095
Missing (%)< 0.1%
Memory size65.9 MiB
Benito Juárez
 
37075
Juárez
 
36996
Venustiano Carranza
 
26492
Cuauhtémoc
 
25606
Emiliano Zapata
 
24805
Other values (2322)
8478349 

Length

Max length77
Median length11
Mean length12.9218437
Min length4

Characters and Unicode

Total characters111506763
Distinct characters64
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAguascalientes
2nd rowAguascalientes
3rd rowAguascalientes
4th rowAguascalientes
5th rowAguascalientes

Common Values

ValueCountFrequency (%)
Benito Juárez37075
 
0.4%
Juárez36996
 
0.4%
Venustiano Carranza26492
 
0.3%
Cuauhtémoc25606
 
0.3%
Emiliano Zapata24805
 
0.3%
Guadalupe23519
 
0.3%
Iztapalapa23450
 
0.3%
Guadalajara22261
 
0.3%
Tijuana21991
 
0.3%
La Paz21947
 
0.3%
Other values (2317)8365181
96.9%

Length

2021-12-23T17:59:26.100098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de1216123
 
7.8%
san903751
 
5.8%
santa259536
 
1.7%
del216482
 
1.4%
la197581
 
1.3%
juã¡rez167911
 
1.1%
el151647
 
1.0%
villa151018
 
1.0%
juan145226
 
0.9%
santiago137677
 
0.9%
Other values (2183)12140863
77.4%

Most occurring characters

ValueCountFrequency (%)
a15641854
 
14.0%
e7853367
 
7.0%
o7456629
 
6.7%
7058492
 
6.3%
l7043022
 
6.3%
n6704546
 
6.0%
t5006962
 
4.5%
i4967471
 
4.5%
c4507510
 
4.0%
u4388645
 
3.9%
Other values (54)40878265
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter84131037
75.4%
Uppercase Letter17087248
 
15.3%
Space Separator7058492
 
6.3%
Other Punctuation1649376
 
1.5%
Format586371
 
0.5%
Other Symbol438784
 
0.4%
Other Number382131
 
0.3%
Other Letter87114
 
0.1%
Math Symbol84348
 
0.1%
Initial Punctuation1862
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Ã3156277
18.5%
S1779544
10.4%
C1699084
9.9%
T1619026
 
9.5%
A1217195
 
7.1%
M1175216
 
6.9%
P715089
 
4.2%
J688937
 
4.0%
G486144
 
2.8%
L474991
 
2.8%
Other values (16)4075745
23.9%
Lowercase Letter
ValueCountFrequency (%)
a15641854
18.6%
e7853367
9.3%
o7456629
8.9%
l7043022
8.4%
n6704546
8.0%
t5006962
 
6.0%
i4967471
 
5.9%
c4507510
 
5.4%
u4388645
 
5.2%
r4067049
 
4.8%
Other values (15)16493982
19.6%
Other Punctuation
ValueCountFrequency (%)
¡1575667
95.5%
.70992
 
4.3%
,2717
 
0.2%
Other Number
ValueCountFrequency (%)
³376075
98.4%
¼6056
 
1.6%
Other Symbol
ValueCountFrequency (%)
©370912
84.5%
67872
 
15.5%
Initial Punctuation
ValueCountFrequency (%)
1568
84.2%
294
 
15.8%
Space Separator
ValueCountFrequency (%)
7058492
100.0%
Format
ValueCountFrequency (%)
­586371
100.0%
Other Letter
ValueCountFrequency (%)
º87114
100.0%
Math Symbol
ValueCountFrequency (%)
±84348
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin101305399
90.9%
Common10201364
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a15641854
15.4%
e7853367
 
7.8%
o7456629
 
7.4%
l7043022
 
7.0%
n6704546
 
6.6%
t5006962
 
4.9%
i4967471
 
4.9%
c4507510
 
4.4%
u4388645
 
4.3%
r4067049
 
4.0%
Other values (42)33668344
33.2%
Common
ValueCountFrequency (%)
7058492
69.2%
¡1575667
 
15.4%
­586371
 
5.7%
³376075
 
3.7%
©370912
 
3.6%
±84348
 
0.8%
.70992
 
0.7%
67872
 
0.7%
¼6056
 
0.1%
,2717
 
< 0.1%
Other values (2)1862
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII105194209
94.3%
None6242820
 
5.6%
Specials67872
 
0.1%
Punctuation1862
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a15641854
14.9%
e7853367
 
7.5%
o7456629
 
7.1%
7058492
 
6.7%
l7043022
 
6.7%
n6704546
 
6.4%
t5006962
 
4.8%
i4967471
 
4.7%
c4507510
 
4.3%
u4388645
 
4.2%
Other values (43)34565711
32.9%
None
ValueCountFrequency (%)
Ã3156277
50.6%
¡1575667
25.2%
­586371
 
9.4%
³376075
 
6.0%
©370912
 
5.9%
º87114
 
1.4%
±84348
 
1.4%
¼6056
 
0.1%
Specials
ValueCountFrequency (%)
67872
100.0%
Punctuation
ValueCountFrequency (%)
1568
84.2%
294
 
15.8%

pop
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2402
Distinct (%)< 0.1%
Missing2095
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean146043.2487
Minimum81
Maximum1922523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.9 MiB
2021-12-23T17:59:26.234741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum81
5-th percentile2974
Q111966
median30097
Q3102149
95-th percentile779566
Maximum1922523
Range1922442
Interquartile range (IQR)90183

Descriptive statistics

Standard deviation300405.6876
Coefficient of variation (CV)2.05696388
Kurtosis12.64746764
Mean146043.2487
Median Absolute Deviation (MAD)23909
Skewness3.390090095
Sum1.260254365 × 1012
Variance9.024357714 × 1010
MonotonicityNot monotonic
2021-12-23T17:59:26.368493image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183548623450
 
0.3%
138562922261
 
0.3%
192252321991
 
0.3%
172121521281
 
0.2%
164535221250
 
0.2%
117335120952
 
0.2%
169218120051
 
0.2%
147649120039
 
0.2%
5054119495
 
0.2%
114299418645
 
0.2%
Other values (2392)8419908
97.5%
ValueCountFrequency (%)
8184
 
< 0.1%
113112
 
< 0.1%
130122
 
< 0.1%
174169
 
< 0.1%
229214
< 0.1%
241216
< 0.1%
245223
< 0.1%
249211
< 0.1%
250445
< 0.1%
296265
< 0.1%
ValueCountFrequency (%)
192252321991
0.3%
183548623450
0.3%
172121521281
0.2%
169218120051
0.2%
164535221250
0.2%
151245016369
0.2%
147649120039
0.2%
138562922261
0.3%
117335120952
0.2%
114299418645
0.2%

pop_male
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2316
Distinct (%)< 0.1%
Missing2095
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean71187.65212
Minimum41
Maximum968740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.9 MiB
2021-12-23T17:59:26.524118image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile1455
Q15829
median14673
Q348818
95-th percentile371794
Maximum968740
Range968699
Interquartile range (IQR)42989

Descriptive statistics

Standard deviation146787.7108
Coefficient of variation (CV)2.061982751
Kurtosis12.88411232
Mean71187.65212
Median Absolute Deviation (MAD)11675
Skewness3.413724763
Sum6.143012438 × 1011
Variance2.154663203 × 1010
MonotonicityNot monotonic
2021-12-23T17:59:26.656979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88765123450
 
0.3%
66699722261
 
0.3%
96874021991
 
0.3%
84667321281
 
0.2%
79854921250
 
0.2%
56387420952
 
0.2%
80948520051
 
0.2%
72059220039
 
0.2%
2453319495
 
0.2%
56480518645
 
0.2%
Other values (2306)8419908
97.5%
ValueCountFrequency (%)
4184
 
< 0.1%
52234
< 0.1%
84169
< 0.1%
109216
< 0.1%
111214
< 0.1%
112217
< 0.1%
117228
< 0.1%
122223
< 0.1%
126211
< 0.1%
133265
< 0.1%
ValueCountFrequency (%)
96874021991
0.3%
88765123450
0.3%
84667321281
0.2%
80948520051
0.2%
79854921250
0.2%
75697716369
0.2%
72059220039
0.2%
66699722261
0.3%
56480518645
0.2%
56387420952
0.2%

pop_female
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2338
Distinct (%)< 0.1%
Missing2095
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean74855.59662
Minimum40
Maximum953783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.9 MiB
2021-12-23T17:59:26.786098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile1532
Q16119
median15461
Q352644
95-th percentile407772
Maximum953783
Range953743
Interquartile range (IQR)46525

Descriptive statistics

Standard deviation153677.1188
Coefficient of variation (CV)2.052981017
Kurtosis12.47229425
Mean74855.59662
Median Absolute Deviation (MAD)12287
Skewness3.371661352
Sum6.459531216 × 1011
Variance2.361665685 × 1010
MonotonicityNot monotonic
2021-12-23T17:59:26.921737image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2600825264
 
0.3%
94783523450
 
0.3%
71863222261
 
0.3%
95378321991
 
0.3%
87454221281
 
0.2%
84680321250
 
0.2%
60947720952
 
0.2%
88269620051
 
0.2%
75589920039
 
0.2%
57818918645
 
0.2%
Other values (2328)8414139
97.5%
ValueCountFrequency (%)
4084
 
< 0.1%
61112
 
< 0.1%
78122
 
< 0.1%
90169
 
< 0.1%
118214
< 0.1%
123434
< 0.1%
132216
< 0.1%
133228
< 0.1%
138217
< 0.1%
151262
< 0.1%
ValueCountFrequency (%)
95378321991
0.3%
94783523450
0.3%
88269620051
0.2%
87454221281
0.2%
84680321250
0.2%
75589920039
0.2%
75547316369
0.2%
71863222261
0.3%
60947720952
0.2%
57818918645
0.2%

afromexican
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct999
Distinct (%)< 0.1%
Missing2095
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3030.690609
Minimum0
Maximum75476
Zeros46216
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size65.9 MiB
2021-12-23T17:59:27.063358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q1100
median408
Q32140
95-th percentile16457
Maximum75476
Range75476
Interquartile range (IQR)2040

Descriptive statistics

Standard deviation7036.067109
Coefficient of variation (CV)2.32160521
Kurtosis26.6464384
Mean3030.690609
Median Absolute Deviation (MAD)374
Skewness4.372176057
Sum2.615280818 × 1010
Variance49506240.36
MonotonicityNot monotonic
2021-12-23T17:59:27.186030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
046216
 
0.5%
241544
 
0.5%
1440131
 
0.5%
1039542
 
0.5%
138721
 
0.4%
2538696
 
0.4%
3437590
 
0.4%
8837079
 
0.4%
636899
 
0.4%
936270
 
0.4%
Other values (989)8236635
95.4%
ValueCountFrequency (%)
046216
0.5%
138721
0.4%
241544
0.5%
331612
0.4%
429025
0.3%
531367
0.4%
636899
0.4%
729630
0.3%
831182
0.4%
936270
0.4%
ValueCountFrequency (%)
7547613908
0.2%
4736621281
0.2%
3907420051
0.2%
3583721991
0.3%
3331323450
0.3%
3179321250
0.2%
2945016369
0.2%
2931720039
0.2%
2830322261
0.3%
2825115357
0.2%

indigenous_language
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1406
Distinct (%)< 0.1%
Missing2095
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4932.190126
Minimum0
Maximum158867
Zeros40349
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size65.9 MiB
2021-12-23T17:59:27.326654image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q198
median906
Q34936
95-th percentile22528
Maximum158867
Range158867
Interquartile range (IQR)4838

Descriptive statistics

Standard deviation10627.88893
Coefficient of variation (CV)2.154801144
Kurtosis47.12021599
Mean4932.190126
Median Absolute Deviation (MAD)890
Skewness5.435034467
Sum4.25614617 × 1010
Variance112952023
MonotonicityNot monotonic
2021-12-23T17:59:27.449326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
364141
 
0.7%
1061737
 
0.7%
455709
 
0.6%
552474
 
0.6%
249766
 
0.6%
1949317
 
0.6%
648295
 
0.6%
1548128
 
0.6%
1347657
 
0.6%
1846844
 
0.5%
Other values (1396)8105255
93.9%
ValueCountFrequency (%)
040349
0.5%
139871
0.5%
249766
0.6%
364141
0.7%
455709
0.6%
552474
0.6%
648295
0.6%
738584
0.4%
832145
0.4%
932789
0.4%
ValueCountFrequency (%)
1588675444
 
0.1%
1189983654
 
< 0.1%
9215812765
0.1%
7104015357
0.2%
668195322
 
0.1%
645556371
0.1%
638006854
0.1%
633923367
 
< 0.1%
612165258
 
0.1%
6008010920
0.1%

metro_area
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.9 MiB
0
6187125 
1
2444293 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8631418
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
06187125
71.7%
12444293
 
28.3%

Length

2021-12-23T17:59:27.572994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-23T17:59:27.636825image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
06187125
71.7%
12444293
 
28.3%

Most occurring characters

ValueCountFrequency (%)
06187125
71.7%
12444293
 
28.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8631418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06187125
71.7%
12444293
 
28.3%

Most occurring scripts

ValueCountFrequency (%)
Common8631418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06187125
71.7%
12444293
 
28.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8631418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06187125
71.7%
12444293
 
28.3%

long
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2469
Distinct (%)< 0.1%
Missing2095
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-99.06487697
Minimum-117.054459
Maximum-86.746092
Zeros0
Zeros (%)0.0%
Negative8629323
Negative (%)> 99.9%
Memory size65.9 MiB
2021-12-23T17:59:27.720410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-117.054459
5-th percentile-106.866044
Q1-101.094629
median-98.884247
Q3-96.999346
95-th percentile-91.981568
Maximum-86.746092
Range30.308367
Interquartile range (IQR)4.095283

Descriptive statistics

Standard deviation4.457194389
Coefficient of variation (CV)-0.04499268081
Kurtosis2.002600813
Mean-99.06487697
Median Absolute Deviation (MAD)1.981661
Skewness-0.4674499972
Sum-854862821.3
Variance19.86658182
MonotonicityNot monotonic
2021-12-23T17:59:27.832112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99.09262323450
 
0.3%
-103.34222222261
 
0.3%
-117.01891321991
 
0.3%
-101.683221281
 
0.2%
-99.04915921250
 
0.2%
-99.11347120952
 
0.2%
-98.19749520051
 
0.2%
-103.38898820039
 
0.2%
-97.60854119495
 
0.2%
-100.31089218645
 
0.2%
Other values (2459)8419908
97.5%
ValueCountFrequency (%)
-117.0544595361
 
0.1%
-117.01891321991
0.3%
-116.626555330
 
0.1%
-116.59513413444
0.2%
-115.9390611901
 
< 0.1%
-115.47557916932
0.2%
-114.7796245937
 
0.1%
-113.5401313248
 
< 0.1%
-112.8572772407
 
< 0.1%
-112.2682753974
 
< 0.1%
ValueCountFrequency (%)
-86.7460922950
 
< 0.1%
-86.82481110920
0.1%
-86.8761772940
 
< 0.1%
-86.9575884524
0.1%
-87.07555297
0.1%
-87.4632053144
 
< 0.1%
-87.4872233249
 
< 0.1%
-87.9373283710
 
< 0.1%
-88.0465875068
0.1%
-88.1522665525
0.1%

lat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2466
Distinct (%)< 0.1%
Missing2095
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean20.24635231
Minimum14.679452
Maximum32.641176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.9 MiB
2021-12-23T17:59:27.960109image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum14.679452
5-th percentile16.459666
Q118.320517
median19.552722
Q321.000822
95-th percentile27.276382
Maximum32.641176
Range17.961724
Interquartile range (IQR)2.680305

Descriptive statistics

Standard deviation3.228445128
Coefficient of variation (CV)0.1594581127
Kurtosis2.41502912
Mean20.24635231
Median Absolute Deviation (MAD)1.351291
Skewness1.490742023
Sum174712313.6
Variance10.42285795
MonotonicityNot monotonic
2021-12-23T17:59:28.092770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.35900423450
 
0.3%
20.67638922261
 
0.3%
32.53246121991
 
0.3%
21.12197221281
 
0.2%
19.59906921250
 
0.2%
19.48294520952
 
0.2%
19.0439920051
 
0.2%
20.72096220039
 
0.2%
16.12989519495
 
0.2%
25.66469718645
 
0.2%
Other values (2456)8419908
97.5%
ValueCountFrequency (%)
14.6794524066
 
< 0.1%
14.7778382830
 
< 0.1%
14.8373142183
 
< 0.1%
14.8630253687
 
< 0.1%
14.91107111074
0.1%
14.940194282
 
< 0.1%
14.9897894584
0.1%
15.018523640
 
< 0.1%
15.0607072887
 
< 0.1%
15.1382894532
0.1%
ValueCountFrequency (%)
32.64117616932
0.2%
32.5726795330
 
0.1%
32.53246121991
0.3%
32.4798315937
 
0.1%
32.3636845361
 
0.1%
31.8663962407
 
< 0.1%
31.80894413444
0.2%
31.74646516369
0.2%
31.3901071504
 
< 0.1%
31.3720681692
 
< 0.1%

esco_avail
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.9 MiB
0
8561620 
1
 
69798

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8631418
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08561620
99.2%
169798
 
0.8%

Length

2021-12-23T17:59:28.215429image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-23T17:59:28.277263image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
08561620
99.2%
169798
 
0.8%

Most occurring characters

ValueCountFrequency (%)
08561620
99.2%
169798
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8631418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08561620
99.2%
169798
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common8631418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08561620
99.2%
169798
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII8631418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08561620
99.2%
169798
 
0.8%

causa_nombre
Categorical

HIGH CARDINALITY

Distinct308
Distinct (%)< 0.1%
Missing1229
Missing (%)< 0.1%
Memory size65.9 MiB
Vive
7939926 
Diabetes mellitus
 
106212
Infarto agudo del miocardio
 
92552
Agresiones (homicidios)
 
28586
Otras enfermedades del hígado
 
24294
Other values (303)
 
438619

Length

Max length149
Median length4
Mean length6.397432432
Min length4

Characters and Unicode

Total characters55211051
Distinct characters57
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowVive
2nd rowVive
3rd rowVive
4th rowVive
5th rowVive

Common Values

ValueCountFrequency (%)
Vive7939926
92.0%
Diabetes mellitus106212
 
1.2%
Infarto agudo del miocardio92552
 
1.1%
Agresiones (homicidios)28586
 
0.3%
Otras enfermedades del hígado24294
 
0.3%
Enfermedades pulmonares obstructivas crónicas22882
 
0.3%
Neumonía21039
 
0.2%
Accidentes de tráfico de vehículos de motor14953
 
0.2%
Enfermedad alcohólica del hígado14026
 
0.2%
Insuficiencia renal13097
 
0.2%
Other values (298)352622
 
4.1%

Length

2021-12-23T17:59:28.381983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vive7939926
72.4%
del256876
 
2.3%
de164476
 
1.5%
enfermedades133058
 
1.2%
y122449
 
1.1%
mellitus106221
 
1.0%
diabetes106221
 
1.0%
agudo100985
 
0.9%
infarto96518
 
0.9%
miocardio92552
 
0.8%
Other values (590)1849527
 
16.9%

Most occurring characters

ValueCountFrequency (%)
e10357543
18.8%
i9582192
17.4%
v8062571
14.6%
V7940122
14.4%
2345634
 
4.2%
a1992038
 
3.6%
o1665905
 
3.0%
s1630582
 
3.0%
r1365397
 
2.5%
d1347096
 
2.4%
Other values (47)8921971
16.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter43204591
78.3%
Uppercase Letter9107819
 
16.5%
Space Separator2345634
 
4.2%
Other Punctuation167461
 
0.3%
Format154523
 
0.3%
Other Number124766
 
0.2%
Other Symbol40464
 
0.1%
Close Punctuation28586
 
0.1%
Open Punctuation28586
 
0.1%
Other Letter8615
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10357543
24.0%
i9582192
22.2%
v8062571
18.7%
a1992038
 
4.6%
o1665905
 
3.9%
s1630582
 
3.8%
r1365397
 
3.2%
d1347096
 
3.1%
n1145658
 
2.7%
t1048151
 
2.4%
Other values (16)5007458
11.6%
Uppercase Letter
ValueCountFrequency (%)
V7940122
87.2%
Ã470824
 
5.2%
I121540
 
1.3%
D116820
 
1.3%
L79725
 
0.9%
O79697
 
0.9%
T77182
 
0.8%
E72213
 
0.8%
A59308
 
0.7%
H23270
 
0.3%
Other values (11)67118
 
0.7%
Other Punctuation
ValueCountFrequency (%)
¡142450
85.1%
,25011
 
14.9%
Space Separator
ValueCountFrequency (%)
2345634
100.0%
Format
ValueCountFrequency (%)
­154523
100.0%
Other Number
ValueCountFrequency (%)
³124766
100.0%
Other Symbol
ValueCountFrequency (%)
©40464
100.0%
Close Punctuation
ValueCountFrequency (%)
)28586
100.0%
Open Punctuation
ValueCountFrequency (%)
(28586
100.0%
Other Letter
ValueCountFrequency (%)
º8615
100.0%
Math Symbol
ValueCountFrequency (%)
±6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin52321025
94.8%
Common2890026
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10357543
19.8%
i9582192
18.3%
v8062571
15.4%
V7940122
15.2%
a1992038
 
3.8%
o1665905
 
3.2%
s1630582
 
3.1%
r1365397
 
2.6%
d1347096
 
2.6%
n1145658
 
2.2%
Other values (38)7231921
13.8%
Common
ValueCountFrequency (%)
2345634
81.2%
­154523
 
5.3%
¡142450
 
4.9%
³124766
 
4.3%
©40464
 
1.4%
)28586
 
1.0%
(28586
 
1.0%
,25011
 
0.9%
±6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII54269403
98.3%
None941648
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e10357543
19.1%
i9582192
17.7%
v8062571
14.9%
V7940122
14.6%
2345634
 
4.3%
a1992038
 
3.7%
o1665905
 
3.1%
s1630582
 
3.0%
r1365397
 
2.5%
d1347096
 
2.5%
Other values (40)7980323
14.7%
None
ValueCountFrequency (%)
Ã470824
50.0%
­154523
 
16.4%
¡142450
 
15.1%
³124766
 
13.2%
©40464
 
4.3%
º8615
 
0.9%
±6
 
< 0.1%

death
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.9 MiB
0
7939926 
1
 
691492

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8631418
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07939926
92.0%
1691492
 
8.0%

Length

2021-12-23T17:59:28.511290image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-23T17:59:28.573127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
07939926
92.0%
1691492
 
8.0%

Most occurring characters

ValueCountFrequency (%)
07939926
92.0%
1691492
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8631418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07939926
92.0%
1691492
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Common8631418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07939926
92.0%
1691492
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8631418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07939926
92.0%
1691492
 
8.0%

Interactions

2021-12-23T17:58:00.675843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:49:21.209871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:50:03.991457image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:50:43.790959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:51:20.317472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:51:57.448341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:52:34.491492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:53:12.409133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:53:47.453884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:54:23.702708image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:55:00.055173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:55:44.502131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:56:28.760462image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:57:15.222002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:58:03.983983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:49:23.907653image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:50:07.800303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:50:46.416597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:51:22.954360image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:52:00.061616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:52:37.242481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:53:14.841387image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:53:50.004117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:54:26.311911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:55:02.680462image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:55:48.029032image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:56:31.629122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:57:18.265005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:58:06.868141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:49:26.582495image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:50:11.815518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:50:48.963389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:51:25.659725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:52:02.698687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:52:40.008643image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:53:17.350842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:53:52.645331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:54:28.893244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:55:05.320771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:55:51.675402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:56:34.675566image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:57:21.258950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:58:09.664640image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:49:29.359066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:50:14.740719image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:50:51.621959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:51:28.216892image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:52:05.402042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:52:42.781492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:53:19.878198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:53:55.241362image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:54:31.521157image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:55:07.918075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:55:55.256399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:56:37.505300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:57:24.358548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:58:12.481123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:49:32.107806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:50:17.626021image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:50:54.251864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:51:30.950692image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:52:07.973671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:52:45.595360image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:53:22.412582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:53:57.890331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:54:34.176089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:55:10.529508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:55:58.918952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:56:40.544558image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:57:27.557813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:58:15.298535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:49:34.906268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:50:20.319813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:50:56.920813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:51:33.706376image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:52:10.694232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:52:48.314122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:53:24.924139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:54:00.509722image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:54:36.790042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:55:13.171594image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:56:02.637734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:56:43.427120image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:57:30.688745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:58:17.922040image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:49:37.574130image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:50:22.871988image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:50:59.412309image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:51:36.354461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-12-23T17:52:13.269039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-12-23T17:57:57.458483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-12-23T17:59:28.650918image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-23T17:59:28.898529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-23T17:59:29.157871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-23T17:59:29.408198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-23T17:59:29.582699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-23T17:58:38.437675image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-23T17:58:46.221520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-23T17:59:07.761345image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-12-23T17:59:12.984634image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexregionsexoedadescolaridadocupacionedo_civilfactorstate_namemunicipiopoppop_malepop_femaleafromexicanindigenous_languagemetro_arealonglatesco_availcausa_nombredeath
001001100.0990.07907AguascalientesAguascalientes948990.0462073.0486917.015170.01839.01-102.29604721.8798231Vive0
111001110.0990.06282AguascalientesAguascalientes948990.0462073.0486917.015170.01839.01-102.29604721.8798231Vive0
221001120.0990.08126AguascalientesAguascalientes948990.0462073.0486917.015170.01839.01-102.29604721.8798231Vive0
331001131.0990.07042AguascalientesAguascalientes948990.0462073.0486917.015170.01839.01-102.29604721.8798230Vive0
441001141.0990.08532AguascalientesAguascalientes948990.0462073.0486917.015170.01839.01-102.29604721.8798230Vive0
551001151.0990.08865AguascalientesAguascalientes948990.0462073.0486917.015170.01839.01-102.29604721.8798230Vive0
661001161.0990.08008AguascalientesAguascalientes948990.0462073.0486917.015170.01839.01-102.29604721.8798230Vive0
771001163.0990.0496AguascalientesAguascalientes948990.0462073.0486917.015170.01839.01-102.29604721.8798230Vive0
881001171.0990.02431AguascalientesAguascalientes948990.0462073.0486917.015170.01839.01-102.29604721.8798230Vive0
991001173.0990.05421AguascalientesAguascalientes948990.0462073.0486917.015170.01839.01-102.29604721.8798230Vive0

Last rows

df_indexregionsexoedadescolaridadocupacionedo_civilfactorstate_namemunicipiopoppop_malepop_femaleafromexicanindigenous_languagemetro_arealonglatesco_availcausa_nombredeath
86314088643301320051328.065.01ZacatecasCalera45759.022579.023180.0650.0191.00-102.70226322.9490370Agresiones (homicidios)1
86314098643302320171194.0991.01ZacatecasGuadalupe211740.0102455.0109285.02849.0524.01-102.51880222.7467820Agresiones (homicidios)1
86314108643303320391188.071.01ZacatecasRío Grande64535.031280.033255.0493.091.00-103.03433223.8274340Agresiones (homicidios)1
86314118643304320201334.0991.01ZacatecasJerez59910.029165.030745.0330.0143.00-102.98992422.6494030Agresiones (homicidios)1
86314128643305320261384.0994.01ZacatecasMazapil17774.09111.08663.0356.06.00-101.55496724.6380970Agresiones (homicidios)1
86314138643306320561274.044.01ZacatecasZacatecas149607.071972.077635.01895.0586.01-102.57183622.7760960Agresiones (homicidios)1
86314148643307320171324.0995.01ZacatecasGuadalupe211740.0102455.0109285.02849.0524.01-102.51880222.7467820Agresiones (homicidios)1
86314158643308320311374.044.01ZacatecasMonte Escobedo8683.04284.04399.058.046.00-103.56164322.3027850Agresiones (homicidios)1
86314168643309320312244.0994.01ZacatecasMonte Escobedo8683.04284.04399.058.046.00-103.56164322.3027850Agresiones (homicidios)1
86314178643310320371259.061.01ZacatecasPánuco17577.08780.08797.078.0126.00-102.54088422.8759640Agresiones (homicidios)1